一种基于雷达图表示的数值型数据的CNN分类方法

A Classification Method of CNN for Numerical Data Based on Radar Chart Representation

卷积神经网络(convolutional neural networks, CNN)是一种广泛用于分析视觉图像的分类方法.由于数值数据存在着非线性、耦合性等复杂的空间关系,因此基于CNN的数值型数据的研究较少.本文的目的是找到一种可行的方法,将CNN的应用领域扩展到数值数据.于是提出了一种基于雷达图表示的数值型数据的CNN分类方法(Radar-CNN).该算法首先将数值数据表示成雷达图形式,然后将其输入CNN中构建分类模型.为了进一步研究特征尺度和序列对性能的影响,提出了两种改进算法Rank Radar-CNN和SFS Radar-CNN.为了验证所提算法的有效性,引入TE化工过程数据集进行实验测试并比较,实验结果表明Radar-CNN及其改进算法具有优异的性能.

Convolutional neural networks(CNN)are a classification method widely used for analyzing visual images. Because of the presence of complex spatial relationships such as nonlinearity and coupling, studies on CNN-based numerical data are limited. The purpose of this paper is to find a feasible way to enable the extension of CNN application to address numerical data. A new CNN classification method based on radar chart(Radar-CNN)is thus proposed. The numerical data are first expressed as radar charts which are fed into CNN to build a classification model. To further investigate the influence of features' scale and sequence on the performance, Rank Radar-CNN and SFS Radar-CNN are also proposed as two variants. Furthermore, to verify the validity of the proposed algorithm, Tennessee Eastman chemical process dataset is introduced for experimental tests. The results show that Radar-CNN and its improved algorithms have excellent performance.